Manufacture of defatted canola meal with enhanced nutritive composition by air classification on an industrial scale
Bibliographic record
Abstract
Abstract BACKGROUND Air classification was used to fractionate canola meal (CM). The effect of combinations of air classification conditions, including rotor speed ranging from 300 to 1200 rpm, air stream rate from 700 to 860 cfpm, and secondary air from 0 to 30%, on particle size and nutritive composition was investigated. Response surface methodology was used to develop response surface equations to estimate these effects. RESULTS Protein concentration increased in almost all the fine fractions. Effects of both secondary air and rotor speed of the air classifier were significant at P ≤ 0.1 and P ≤ 0.01, respectively. Almost all the fine fractions contained more oil. Rotor speed, air stream rate, and their interaction were significant at P ≤ 0.01. Both acid detergent fiber (ADF) and neutral detergent fiber (NDF) were shifted to the coarse fractions during the air classification. The rotor speed was significant for both ADF and NDF at P ≤ 0.01. CONCLUSIONS Protein shifted to the fine fractions. The highest protein concentration in the fine CM fraction was estimated to be 404.2 g kg−1, demonstrating an enhancement of 10.7%. ADF and NDF shifted to the coarse CM fractions and were respectively estimated to be 294.8 and 332 g kg−1, which were increased by 52.7% and 43.7%. Oil shifted to the fine fractions. The highest concentration was predicted to be 59.7 g kg−1, increased by 45.6%. Fine or coarse fraction yields were estimated to be 35–50%, indicating that they are acceptable for an industrial manufacture. © 2019 Society of Chemical Industry
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".